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Illustration: Aïda Amer/Axios

AI is speeding up the discovery of the structure of proteins that drive biological processes across organisms.

Why it matters: If researchers can predict what shape a protein will take, they can better understand how it works — and potentially target medicines for proteins that cause disease or create antibiotics that can disable resistant bacteria's proteins.

The big picture: Determining the structure of proteins is typically done through painstaking experiments involving crystallizing proteins and analyzing them with X-rays. That's yielded the shapes of a small fraction of proteins in humans.

  • But new machine-learning systems like AlphaFold and RoseTTAFold are making a "once in a generation" advance and have been able to speed up the discovery process greatly.
  • "If you really want to understand how biology works at the molecular level — and that is really where it works, with little machines interacting with each other — you need to know the shape of the protein molecules," said John Moult, a structural biologist at the University of Maryland, Shady Grove.
  • "For a long time we've known the sequences at the DNA level of all the genes, and those sequences dictate what these shapes are. But the way in which you can get from 'here's the sequence' to 'what's the detailed shape' has been an outstanding computational problem for more than 50 years," Moult told Axios.
  • Moult is also co-founder of the Critical Assessment of Structure Prediction, or CASP, which is a challenge that's been run for 25 years to test modeling programs predicting protein structures.

The latest: Google DeepMind's AlphaFold2 system is able to issue a “good prediction" of protein structures about 95% of the time, scientists said at a joint press conference announcing DeepMind and the European Molecular Biology Laboratory (EMBL)'s collaboration.

  • Using AlphaFold 2, scientists were able to generate 3D models of 350,000 proteins, 36% of which have a "high confidence," they said. The researchers have opened these structures to use via the AlphaFold database in an "effort to move the science forward," said DeepMind's Demis Hassabis, co-author of the paper in Nature.
  • Another co-author, Centre for Enzyme Innovation director John McGeehan, said they had DeepMind test seven enzymes in their experiments to break down plastics, two of which they had already found experimental structures. DeepMind quickly confirmed the two structures and gave further information on the others, too.
  • "It's one of those moments, to be honest, where the hair stood up at the back of my neck," McGeehan said. "The structures that [GoogleMind] produced were identical to our crystal structures. In fact, they contained even more information than the crystal structures were able to provide [using traditional methods]. ... The acceleration to our project is multiple years."

The interpretation of rare genetic mutations is another area AI is expected to target, said EMBL deputy director and co-author Ewan Birney.

  • "This is a very practical problem in clinical genetics, where you have a suspected series of mutations or changes in an affected child, and you want to try and work out which one is likely to be the reason why your child has got a particular genetic disease."

Context: The AlphaFold2 findings follow last week's announcement that the University of Washington created a neural network, RoseTTAFold, to determine protein structures and published several openly via GitHub.

What's next: "This is just the first clear demonstration of  the power of AI in biology," Moult said.

  • "There are obvious next things that are likely to happen, to do with how proteins interact and drug development."
  • "Our understanding of protein structures and associated biology will take a big leap forward as people build on these resources," Moult said.

Go deeper: An AI answers one of biology's biggest problems

Go deeper

Bezos beats Branson in space billionaires' battle for attention

Photo illustration: Aïda Amer/Axios. Photo: Imtiyaz Shaikh (Anadolu Agency), Drew Angerer/Getty Images

Jeff Bezos' flight into space generated more interest from the public than Richard Branson's, and both billionaires overshadowed their respective space companies.

Why it matters: Data shows an outsized public interest in the personalities at the center of the space trips, compared to the companies behind them — which could reinforce public suspicion that the ventures were partly vanity plays.

Updated 5 hours ago - Sports

Swimmer Chase Kalisz first American to win Olympics gold medal

Chase Kalisz of Team United States celebrates after winning the Men's 400m Individual Medley Final on day two of the Tokyo 2020 Olympic Games at Tokyo Aquatics Centre in Tokyo, Japan. Photo: Al Bello/Getty Images

Swimmer Chase Kalisz became on Sunday the first Team USA Olympian to win gold at the Tokyo Games.

The big picture: The Rio 2016 silver medalist's winning time in the men's 400 meters Individual Medley Final was 4 minutes 9.42 seconds. His teammate Jay Litherland took silver .86 seconds later.

California's largest wildfire razes homes as 88 huge blazes burn in U.S.

Firefighters on the scene as dozens of homes burn during the Dixie Fire in the Indian Falls neighborhood of unincorporated Plumas County, California, on July 24. Photo: Josh Edelson/AFP via Getty Images

Flames from California's biggest wildfire were engulfing homes in the state's north overnight — one of 88 large blazes raging in the U.S.

Driving the news: The Dixie Fire, which erupted July 14 near the origin of the deadly 2018 Camp Fire in Butte County, was tearing through the community of Indian Falls in the neighboring Plumas County, per AP.

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